Rectangular Wall Loss Strategy (interactive quick start)¶

This notebook shows how to use particula.dynamics.RectangularWallLossStrategy to compute wall loss rates and apply time stepping in a box-shaped chamber. Runtime is kept small (<1 s) with tiny arrays and short integration horizons.

Prerequisites¶

• Python 3.9+
• particula installed (pip or conda)
• NumPy for light array handling

To run locally:

pip install particula

Imports and helpers¶

In [ ]:

import numpy as np
import particula as par

np.set_printoptions(precision=3, suppress=True)

import numpy as np import particula as par np.set_printoptions(precision=3, suppress=True)

1) Build a discrete particle distribution¶

We use the preset radius-binned distribution to keep setup minimal.

In [ ]:

particle = par.particles.PresetParticleRadiusBuilder().build()
print("Bins:", particle.get_radius().size)
print("Initial total concentration:", f"{particle.get_total_concentration():.3e} 1/m^3")

particle = par.particles.PresetParticleRadiusBuilder().build() print("Bins:", particle.get_radius().size) print("Initial total concentration:", f"{particle.get_total_concentration():.3e} 1/m^3")

2) Configure RectangularWallLossStrategy¶

Dimensions are given as (x, y, z) in meters.

In [ ]:

wall_loss = par.dynamics.RectangularWallLossStrategy(
    wall_eddy_diffusivity=1e-3,
    chamber_dimensions=(1.0, 1.2, 0.8),
    distribution_type="discrete",
)
T = 298.15  # K
P = 101325.0  # Pa

wall_loss = par.dynamics.RectangularWallLossStrategy( wall_eddy_diffusivity=1e-3, chamber_dimensions=(1.0, 1.2, 0.8), distribution_type="discrete", ) T = 298.15 # K P = 101325.0 # Pa

3) Compute instantaneous wall loss rate¶

This returns a per-bin rate array (1/s).

In [ ]:

rate = wall_loss.rate(particle=particle, temperature=T, pressure=P)
print("Rate shape:", rate.shape)
print("First three rates (1/s):", rate[:3])

rate = wall_loss.rate(particle=particle, temperature=T, pressure=P) print("Rate shape:", rate.shape) print("First three rates (1/s):", rate[:3])

4) Apply a short transient (30 min, 5 s steps)¶

Keeps runtime small while showing concentration decay.

In [ ]:

dt = 5.0  # seconds
steps = int((30 * 60) / dt)
history = np.zeros(steps + 1)
history[0] = particle.get_total_concentration()

for i in range(1, steps + 1):
    particle = wall_loss.step(
        particle=particle, temperature=T, pressure=P, time_step=dt
    )
    history[i] = particle.get_total_concentration()

print("Final concentration:", f"{history[-1]:.3e} 1/m^3")
print("Relative decay:", f"{history[-1] / history[0]:.3f}")

dt = 5.0 # seconds steps = int((30 * 60) / dt) history = np.zeros(steps + 1) history[0] = particle.get_total_concentration() for i in range(1, steps + 1): particle = wall_loss.step( particle=particle, temperature=T, pressure=P, time_step=dt ) history[i] = particle.get_total_concentration() print("Final concentration:", f"{history[-1]:.3e} 1/m^3") print("Relative decay:", f"{history[-1] / history[0]:.3f}")

Optional: quick visualization¶

Uncomment to view a normalized decay curve (kept lightweight).

In [ ]:

# from matplotlib import pyplot as plt
# minutes = np.arange(steps + 1) * dt / 60.0
# plt.plot(minutes, history / history[0])
# plt.xlabel("Time (min)")
# plt.ylabel("Normalized concentration")
# plt.title("Rectangular wall loss (1.0 x 1.2 x 0.8 m)")
# plt.grid(True)
# plt.tight_layout()
# plt.show()

# from matplotlib import pyplot as plt # minutes = np.arange(steps + 1) * dt / 60.0 # plt.plot(minutes, history / history[0]) # plt.xlabel("Time (min)") # plt.ylabel("Normalized concentration") # plt.title("Rectangular wall loss (1.0 x 1.2 x 0.8 m)") # plt.grid(True) # plt.tight_layout() # plt.show()

5) Notes on other distribution types¶

The same API works for "continuous_pdf" and "particle_resolved". Only the underlying particle representation changes.

In [ ]:

# Continuous PDF example (same call pattern)
wall_loss_pdf = par.dynamics.RectangularWallLossStrategy(
    wall_eddy_diffusivity=1e-3,
    chamber_dimensions=(1.0, 1.2, 0.8),
    distribution_type="continuous_pdf",
)
# rate_pdf = wall_loss_pdf.rate(particle, temperature=T, pressure=P)

# Particle-resolved example
resolved = par.particles.PresetResolvedParticleMassBuilder().build()
wall_loss_resolved = par.dynamics.RectangularWallLossStrategy(
    wall_eddy_diffusivity=1e-3,
    chamber_dimensions=(1.0, 1.2, 0.8),
    distribution_type="particle_resolved",
)
# resolved = wall_loss_resolved.step(
#     particle=resolved, temperature=T, pressure=P, time_step=dt
# )

# Continuous PDF example (same call pattern) wall_loss_pdf = par.dynamics.RectangularWallLossStrategy( wall_eddy_diffusivity=1e-3, chamber_dimensions=(1.0, 1.2, 0.8), distribution_type="continuous_pdf", ) # rate_pdf = wall_loss_pdf.rate(particle, temperature=T, pressure=P) # Particle-resolved example resolved = par.particles.PresetResolvedParticleMassBuilder().build() wall_loss_resolved = par.dynamics.RectangularWallLossStrategy( wall_eddy_diffusivity=1e-3, chamber_dimensions=(1.0, 1.2, 0.8), distribution_type="particle_resolved", ) # resolved = wall_loss_resolved.step( # particle=resolved, temperature=T, pressure=P, time_step=dt # )

Summary¶

• Configure rectangular chambers with chamber_dimensions (x, y, z).
• Use rate for instantaneous loss coefficients.
• Use step to update particle concentrations over time.
• Switch distribution_type to target discrete, continuous PDF, or particle-resolved representations.